Physical Poster + E-Poster Presentation 34th Lorne Cancer Conference 2022

Modelling colorectal cancer using patient-derived organoid lines to personalise cancer medicine (#134)

Rebekah Engel 1 2 3 , Thierry Jarde 2 3 4 , Karen Oliva 1 , Genevieve Kerr 2 3 , Wing Hei Chan 2 3 , Stuart Archer 5 , Sara Hlavca 2 3 , Christine Koulis 1 , Paul J McMurrick 1 , Helen E Abud 2 3
  1. Cabrini Monash University Department of Surgery, Cabrini Health, Malvern, Victoria, Australia
  2. Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia
  3. Cancer Program, Monash Biomedicine Discovery Institute, Clayton, Victoria, Australia
  4. Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia
  5. Monash Bioinformatics Platform, Monash University, Clayton, Victoria, Australia

Background: Colorectal cancer is the third most commonly diagnosed and the second leading cause of cancer death in Australia. To improve outcomes for these patients, we need to develop new treatment strategies. Personalised cancer medicine, where patients are treated based on the characteristics of their own tumour has gained significant interest for its promise to improve outcomes and reduce unnecessary side effects.

Aim: The purpose of this study was to examine the potential utility of patient-derived colorectal cancer organoids (PDCOs) in a personalised cancer medicine setting.

Methods: PDCOs were established from tissue obtained from treatment-naïve patients undergoing surgical resection for the treatment of CRC. We examined the recapitulation of key histopathological, molecular and phenotypic characteristics of the primary tumour. We also examined response to chemotherapeutic drugs that are commonly used for the treatment of CRC.

Results: We have created a bio-resource of PDCOs from primary and metastatic CRCs. Key histopathological features were retained in PDCOs when compared to the primary tumour. Additionally, a cohort of PDCOs were characterised through whole exome sequencing, exhibiting a high level of concordance in key driver mutations when compared to the primary tumour. We are also utilising PDCOs in drug response assays, with the aim of developing a pre-clinical test that can predict treatment outcomes for CRC patients, before they undergo therapy.

Conclusions: PDCOs recapitulate characteristics of the tissue from which they are derived and are a powerful tool for cancer research. Further research will determine their utility for predicting patient outcomes in a personalised cancer medicine setting.